Computer vision (CV) is used in many forms and across a variety of applications. The image data fed into CV algorithms can be used for object recognition. With CV-based object recognition algorithms analyze the image data to identify an object among a plurality of objects in an image. For example, image data of a vehicle could be analyzed by a CV algorithm to identify an engine among the plurality of objects making up the vehicle. The CV algorithms may, in additional rounds of processing, analyze the identified object to identify a specific component of the object. Continuing with the engine example, engine image data could be analyzed to identify a specific valve in the engine.
Computer aided maintenance (CAM) is an application that uses image data for object recognition using CV. Once the image data is used to identify a component, a maintenance procedure for the component, such as repair or replace, may be desired. In support of this, CAM applications generally combine CV algorithms with augmented reality (AR) algorithms to provide, on some type of display device, information and/or instructions associated with the identified component, such as the desired maintenance procedures. In some cases, CAM applications also record activities (i.e., the performed maintenance procedures) responsive to the provided instructions, for later inspection.
A technological problem is presented when the image data is complex or comprises a plurality of objects. Although CV algorithms continue to be developed, using computer vision to detect a specific object among a large number of objects can be very computationally and memory intensive. The user experiences the extensive computations as a long object recognition time. The CV system experiences the extensive computations as a large power drain. In addition to long object recognition times, conventional CV systems lack other desirable technological features, such as a capability of determining a distance or spatial relationship between a host and the recognized object. The provided systems and methods for enhanced computer vision improve upon existing CV systems by addressing these technological problems in an unconventional way, in addition to providing other technological enhancements.